Suicide prevention training within graduate psychology programs in Quebec, Canada: A quantitative and qualitative survey.
Bibliographic record
Abstract
Each year, over 700,000 individuals die by suicide worldwide. Both the Canada and Quebec National Suicide Prevention Strategies advocate suicide prevention training for all mental health professionals. We conducted a mixed-methods survey, qualitative and quantitative, to document the needs and barriers regarding suicide prevention training within Graduate Studies Programs in Psychology in Quebec, Canada, in 2023. Of the 27 programmes/streams in French or English institutions and accredited by the Quebec Order of Psychologists, 21 (78%) participated, represented by 12 programme or clinic directors with several years’ experience in their role. Semistructured interviews lasted 13–56 min (median 32 min); recordings were translated to English as necessary. We analyzed themes along four domains: suicide risk assessment and management, competing needs, challenges in providing training, and implementing standardized training. Of 21 participating programmes/streams, 19 (90%) provided training on suicide risk assessment and management. Representatives felt students were well prepared to assess suicide risk but less so to manage individuals contemplating suicide (interventions, safety plan). All curricula included core competencies, such as building empathic relationships, but skills like coordinating care with other professionals and working with minorities or neurodivergent populations were insufficiently covered. Barriers included cost and time constraints. Almost 40% of representatives supported province-wide standardized preinternship training for all future psychologists; another 25% disagreed, citing institutional autonomy. Standardized suicide prevention training would provide all psychologists with the skills to assess and manage individuals contemplating suicide, including a shared language with other health care professionals to facilitate coordinated care and services within the community.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".